Bangla MEG SaVANT Analysis

Author

Swarnendu Moitra

Code 
path <- getwd()
level_order <- c('Gramm', 'CatViol', 'SemViol')

Load Libraries

Code 
library(lme4)
library(tidyverse)
library(glmm)
library(ggplot2)
library(afex)
library(trimr)
require(car)
require(lattice)
require(itsadug)
library(ggpubr)
library(languageR)
library(plotrix)
library(Hmisc)
library(rlist)
library(effects)
library(sjPlot)
library(effectsize)
library(trimr)
library(jtools)
library(plotrix)
library(ggpubr)
library(gridExtra)
library(grid)
library(ggthemes)
source('data/Themes.R')

Behavioural

Online

MEG

PRE DECOMPOSITION

Tark Localiser

6 conditions in this experiment, with 50 trials each. These conditions vary in length and Gaussian noise level as a visual mask. The trials were either 1-element or 4-element items. The 1-element items included 4 unique one-symbol items presented at the lowest Gaussian noise level (level 1), and 26 unique one-letter items presented at both the lowest (level 1) and highest Gaussian noise level (level 24). The one-symbol items were a triangle, a square, a diamond, or a circle each containing a bar-diacritic over the element and the one-letter items consisted single Bangla consonants. The 4-element items were 4-symbol strings presented only at the lowest Gaussian level (level 1) and 50 4-letter monomorphemic Bangla words presented at both noise level 1 and level 24. The four symbols used were again triangle, square, diamond, and circle, and were presented in four different permutations, joined together by a bar. Taking together we had a total of 300 trials in this experiment.

Analysis below is follwing Gwillams et al 2016

Length: 1 & 4

Noise: Level 1 & Level 24

StimulusType: symbol & letter

We removed 1 participant resulting in N=23 for the following Tark analysis MEG 92 sensor was not included in the analyses

Grand Average Plot

Grand Average Plot: Tark

Left Posterior Channels

Right Posterior Channels

TYPE I Noise Effect

Noise Level was coded as follows: 1: 0, 24: 1;

Stimulus Type was coded as follows: letter: 0, word: 1.

Time window : 80-130ms

params

samples = 10000
pmin = 0.05
mintime = 0.020
minsource = 20

NO SIGNIFICANT CLUSTERS WERE IDENTIFIED for Type I noise

Consequently no analyses were performed for Type II Noise effect.

TYPE II letter response (130 - 180)ms - Bilateral occipital and temporal regions

StringType and Stimulus Type as predictors. String Type was coded as follows: symbols: 0, letters: 1; Stimulus Type was coded as follows: one element: 0, four elements:1 between 130-180ms in Bilateral Occipital and Temporal Regions.

String Type was coded as follows: symbols: 0, letters: 1;

Stimulus Type was coded as follows: one element: 0, four elements:1

Time window : 130-180ms

Only One CLUSTER WAS IDENTIFIED

Cluster:

130-161ms
vertices=1781.88
p=0.0943
Code 
file_ws_rh <- "data/TC_Tark_RH.csv"
ws_min_rh = 130
ws_max_rh = 161
p_ws_rh = 0.0943


data <- read_csv(file_ws_rh, col_names = c("Time", "Participant", "Item.no", "cond", "type","hemi", "dSPM"))
data$Condition <- paste((data$cond),(data$type))
data$Time <- data$Time*1000
#unique(data$Condition)
wordVsymbols_rh <- data %>% filter(Participant != "B0025") %>%  filter(((cond == "word" & type =="clean") | (cond == "word" & type =="symbols") ))
wordVsymbols_rh$Condition[wordVsymbols_rh$Condition == "word clean" ] <- "Letter"
wordVsymbols_rh$Condition[wordVsymbols_rh$Condition == "word symbols" ] <- "Symbol"
wordVsymbols_rh <- wordVsymbols_rh %>% 
                  dplyr::group_by(
                    Time,
                    Condition,
                    hemi
                  ) %>%
                  dplyr::summarise(
                    SE = std.error(dSPM),
                    dSPM = mean(dSPM)
                  )
wordVsymbols_rh <- wordVsymbols_rh %>% filter(hemi == "dSPM-rh.stc")
#wordVsymbols_lh$Condition = with(wordVsymbols_lh, reorder(Word, Symbols)
wordVsymbols_Bar_rh <- wordVsymbols_rh %>% filter(Time>= ws_min_rh & Time <= ws_max_rh) %>% dplyr::group_by(Condition) %>% dplyr::summarise(SE = std.error(dSPM), dSPM = mean(dSPM)) 
wordVsymbols_Bar_rh <- wordVsymbols_Bar_rh %>% mutate(Condition = as.factor(Condition))
wordVsymbols_Bar_rh$Condition <- relevel(wordVsymbols_Bar_rh$Condition, ref = "Letter")
Code 
knitr::kable(wordVsymbols_Bar_rh)
Condition SE dSPM
Letter 0.0825430 -0.7949181
Symbol 0.0958094 -0.1588367
Code 
wordVsymbols_timeseries_rh <- ggplot(wordVsymbols_rh, aes(x = Time, y = dSPM, col = Condition, linetype = Condition)) + 
  geom_line(aes(group = Condition),size = 1) +
  
  
  #annotate('rect',xmin=tmin, xmax=tmax, alpha=0.12,ymin=-Inf,ymax=Inf,fill="darkgrey") + 
  annotate('text',x=(ws_min_rh + ws_max_rh)/2, y= 0.7, label ="*", size = 20) + 
  annotate('rect',xmin=ws_min_rh, xmax=ws_max_rh, alpha=0.2,ymin=-Inf,ymax=Inf,fill="darkgrey") + 
  # annotate('rect',xmin=tmin - 50, xmax=tmax + 50, alpha=0.1,ymin=-Inf,ymax=Inf,fill="green") + 
  # annotate('rect',xmin=-Inf, xmax=tmin, alpha=0.075,ymin=-Inf,ymax=Inf,fill="#000000") + 
  # annotate('rect',xmin=tmax, xmax=Inf, alpha=0.075,ymin=-Inf,ymax=Inf,fill="#666666") + 
  # scale_colour_manual("",values=c("#E63946","#276FBF",  "#545083", "#FF8888", "#FF0000", "#FF8888")) +
  scale_colour_manual("",values=c("deeppink","dodgerblue",  "#545083", "#FF8888", "#FF0000", "#FF8888")) +
  scale_linetype_manual("",values=c("solid", "twodash", "solid", "dashed", "solid", "dashed")) +
  # scale_fill_manual("",values=c("#E63946","#545083",  "#545083", "#FF8888", "#FF0000", "#FF8888")) +
  geom_ribbon(aes(ymin = dSPM - SE, ymax = dSPM + SE, fill = Condition, group=Condition), alpha = 0.2, col = NA, show.legend = FALSE) + 
  scale_y_continuous("Activation (dSPM)",expand=c(0,0), limits = c(-1.5, 1.5)) + scale_x_continuous("Time (ms)", expand=c(0,0), limits = c(-50, 350)) + theme(legend.position="bottom") +
   theme_minimal() + theme(panel.grid.major = element_blank(),
                                                   panel.grid.minor = element_blank(),
                                                   axis.title = element_text(size = 14, face = "bold"),
                                                   axis.text = element_text(size = 12), legend.position = "none") +
  
  geom_hline(yintercept=0, linetype="dotted") + geom_vline(xintercept=0, linetype="dotted")

wordVsymbols_bar_rh <- ggplot(wordVsymbols_Bar_rh, aes(x=Condition, y=dSPM,fill=Condition)) + 
  geom_bar(stat = "identity", width = 0.7, position = position_dodge(0.7), show.legend = FALSE, color="#545083"  )+
  #scale_x_discrete(limits = Conditions) +  facet_wrap( ~Prefix) +
  ylab("dSPM") + 
  xlab("Conditions") +
  scale_fill_manual("",values=c("deeppink","dodgerblue",  "#545083", "#FF8888", "#FF0000", "#FF8888")) +
  #theme_Publication()+
  # coord_cartesian(ylim = c(0, 1150)) +
  # scale_y_continuous(expand = expansion(mult = c(0, 0.05)))+
  # geom_signif(
  #   comparisons = list(c("Grammatical", "Pseudowords")),
  #   margin_top = 0.8,
  #   step_increase = 0.09,
  #   tip_length = 0.5,
  #   annotation = c("***")
  # )+
   geom_hline(yintercept=0, linetype="dotted") + geom_vline(xintercept=0, linetype="dotted") +
   theme_minimal() + theme(panel.grid.major = element_blank(),
                                                   panel.grid.minor = element_blank(),
                                                   axis.title = element_text(size = 14, face = "bold"),
                                                   axis.text = element_text(size = 12)) +
  #geom_errorbar function is used to plot error bars
  geom_errorbar(aes(ymin=dSPM-SE,
                     ymax=dSPM+SE,
                 width=0.3))
Code 
Plot_wordSymbol_rh <-grid.arrange(wordVsymbols_bar_rh + theme_Publication_SwarMoi() ,(wordVsymbols_timeseries_rh + theme_Publication_SwarMoi() +theme(legend.position = "none")),nrow=1, widths=c(4,9))

RH Cluster 130-161ms | p=0.0943 Decomposition

Lexical Statistics generated from LexiVault + indicNLP

Corpus Overview: indicNLP

  • Language: Bengali (bn)

  • Corpus Size: 7.2 million sentences, 100 million tokens

Data Processing Steps:

  1. Corpus Preparation:

    • The raw corpus was first subjected to normalization and cleaning procedures.

    • The cleaned corpus was then divided into manageable chunks to facilitate further processing.

  2. Part-of-Speech Tagging (POS Tagging):

    • POS tagging was performed using the BNLP tool available at BNLP GitHub repository.

    • As of September 17, 2024, 3.2 million sentences from the corpus have been successfully POS tagged.

  3. Stemming:

    • Depending on the POS tags assigned, tokens were stemmed using the LexiVault Rule-Based Stemmer, accessible at LexiVault GitHub repository.

  4. Data Extraction and Normalization:

    • Stimuli containing both stemmed and whole-word forms were queried and extracted from the tagged corpus.

    • To ensure consistency, token counts were normalized using per million counts.

Current Status:

  • As of September 17, 2024, a total of 3.2 million sentences have been POS tagged and stemmed from the Bengali corpus.
Corpus Overview: SketchEngine [bnTenTen21]

  • Language: Bengali (bn)

  • Corpus Size: 40mil sentences, 541 mil tokens

  1. Data Extraction and Normalization:

    • The stimuli were queried and the Per Million counts are extracted

The total frequency counts per million were aggregated, and a regressor file was prepared. We verified that the WholeWordFreq counts were less than the StemFreq counts. This data was then used in regression analysis and for generating correlation plots.

Left Fusiform gyrus (160 - 179ms) p=0.01

Left Fusiform gyrus (160-179ms) p=0.01

POST DECOMPOSITION

X <- tribble( ~Prefix, ~Condition, ~PrefixType, “CatViolDU”, “CatViol”, “DU”, “CatViolPROTI”, “CatViol”,“PROTI”, “GrammDU”, “Gramm”,“DU”, “GrammPROTI”, “Gramm”,“PROTI”, “SemViolDU”, “SemViol”,“DU”, “SemViolPROTI”, “SemViol”,“PROTI”, “Filler”, “Filler”,“Fill”, )

TL: RH_TemporalLobe_244-286_p0.0197

Code 
RH_Temporal <- read_csv("data/SAVANT/CatViol_RH_244-286_p0.0197.csv", col_names = c("Participant", "Prefix","hemi", "dSPM"))
RH_Temporal = RH_Temporal %>% left_join(X)
RH_Temporal <- RH_Temporal %>% select(!hemi)  %>% group_by(Condition) %>% summarise(SE = std.error(dSPM),dSPM = mean(dSPM))
RH_Temporal_TC <- read_csv("data/SAVANT/TC_CatViol_RH_244-286_p0.0197.csv", col_names = c("time", "Participant", "Prefix","hemi", "dSPM"))
RH_Temporal_TC = RH_Temporal_TC %>% left_join(X)

RH_Temporal_TC$time = RH_Temporal_TC$time*1000

RH_Temporal_TC <- RH_Temporal_TC %>% filter(between(time, 244, 286)) %>% group_by(Condition) %>% summarise(SE = std.error(dSPM),
                    dSPM = mean(dSPM))

RH_temporal_plot <- RH_Temporal %>% filter(Condition != "Filler")  %>% ggplot(aes(x= factor(Condition, level = level_order),y=dSPM,fill=Condition)) +
  # geom_bar function is used to plot bars of barplot
  geom_bar(stat = "identity", width = 0.95, position = position_dodge(0.1), show.legend = FALSE  )+
  #scale_x_discrete(limits = Conditions) +  facet_wrap( ~Prefix) +
  ylab("dSPM") + 
  xlab("Conditions") +
  scale_fill_manual(values = c("blue" , "green" ,"red"))+
   geom_errorbar(aes(ymin=dSPM-SE,
                    ymax=dSPM+SE,
                 width=0.3))+ 
  theme_minimal() +
  theme(axis.text.x = element_text(size = 15, angle = 0, hjust = .5, vjust = .5, face = "plain"),
        axis.text.y = element_text(size = 15, angle = 0, hjust = 1, vjust = 0, face = "plain"),  
        axis.title.x = element_text(size = 18, angle = 0, hjust = .5, vjust = 0, face = "plain"),
        axis.title.y = element_text(size = 18, angle = 90, hjust = .5, vjust = .5, face = "plain"))

RH_temporal_plot

Code 
knitr::kable(RH_Temporal)
Condition SE dSPM
CatViol 0.0711853 0.3463524
Filler 0.0794799 -0.0110849
Gramm 0.0786636 -0.0136021
SemViol 0.0952327 -0.1208228

OF : LH OF 424-452_p0.0472

Code 
LH_OF <- read_csv("data/SAVANT/OF_LH_424-452_p0.0472.csv", col_names = c("Participant", "Prefix","hemi", "dSPM"))
LH_OF = LH_OF %>% left_join(X)
LH_OF <- LH_OF %>% select(!hemi)  %>% group_by(Condition,Prefix, PrefixType) %>% summarise(SE = std.error(dSPM),
                    dSPM = mean(dSPM))
LH_OF_TC <- read_csv("data/SAVANT/TC_OF_LH_424-452_p0.0472.csv", col_names = c("time", "Participant", "Prefix","hemi", "dSPM"))
LH_OF_TC = LH_OF_TC %>% left_join(X)
LH_OF_TC$time = LH_OF_TC$time*1000
LH_OF_TC <- LH_OF_TC %>% filter(between(time, 424, 452)) %>% group_by(Condition, Prefix, PrefixType) %>% summarise(SE = std.error(dSPM),
                    dSPM = mean(dSPM))


LH_OF_plot <- LH_OF %>% filter(Condition != "Filler")  %>% ggplot(aes(x= factor(Condition, level = level_order),y=dSPM,fill=Condition)) +
  # geom_bar function is used to plot bars of barplot
  geom_bar(stat = "identity", width = 0.99, position = position_dodge(0.1), show.legend = FALSE  )+
  #scale_x_discrete(limits = Conditions) +  facet_wrap( ~Prefix) +
  ylab("dSPM") + 
  xlab("Conditions") +
  facet_wrap(~PrefixType) +
  # scale_fill_manual(values = c("blue", "lightblue" , "green", "lightgreen" ,"red", "pink"))+
   scale_fill_manual(values = c("blue",  "green","red"))+
   geom_errorbar(aes(ymin=dSPM-SE,
                    ymax=dSPM+SE,
                 width=0.3))+ 
  theme_minimal() +
  theme(axis.text.x = element_text(size = 18, angle = 0, hjust = .5, vjust = .5, face = "plain"),
        axis.text.y = element_text(size = 15, angle = 0, hjust = 1, vjust = 0, face = "plain"),  
        axis.title.x = element_text(size = 18, angle = 0, hjust = .5, vjust = 0, face = "plain"),
        axis.title.y = element_text(size = 18, angle = 90, hjust = .5, vjust = .5, face = "plain"))

LH_OF_plot

Code 
knitr::kable(LH_OF)
Condition Prefix PrefixType SE dSPM
CatViol CatViolDU DU 0.0713381 -0.1174651
CatViol CatViolPROTI PROTI 0.2385963 0.4050528
Filler Filler Fill 0.0655281 0.1002821
Gramm GrammDU DU 0.1590589 0.2822480
Gramm GrammPROTI PROTI 0.0938074 -0.1899715
SemViol SemViolDU DU 0.1309587 0.2741620
SemViol SemViolPROTI PROTI 0.1841838 -0.4534022

Exploratory Analysis

OF : LH_OF Early (238-279_p0.0049)

Code 
LH_OF_early  <- read_csv("data/SAVANT/CatViol_LH_earlyOF_238-279_p0.0049.csv", col_names = c("Participant", "Prefix","hemi", "dSPM"))
LH_OF_early = LH_OF_early %>% left_join(X)

LH_OF_early <- LH_OF_early %>% select(!hemi)  %>% group_by(Condition) %>% summarise(SE = std.error(dSPM),
                    dSPM = mean(dSPM))
LH_OF_early_TC <- read_csv("data/SAVANT/TC_CatViol_LH_earlyOF_238-279_p0.0049.csv", col_names = c("time", "Participant", "Prefix","hemi", "dSPM"))
LH_OF_early_TC = LH_OF_early_TC %>% left_join(X)
LH_OF_early_TC$time = LH_OF_early_TC$time*1000
LH_OF_early_TC <- LH_OF_early_TC %>% filter(between(time, 238, 279)) %>% group_by(Condition) %>% summarise(SE = std.error(dSPM),
                    dSPM = mean(dSPM))

LH_OF_early_plot <- LH_OF_early %>% filter(Condition != "Filler")  %>% ggplot(aes(x= factor(Condition, level = level_order),y=dSPM,fill=Condition)) +
  # geom_bar function is used to plot bars of barplot
  geom_bar(stat = "identity", width = 0.95, position = position_dodge(0.1), show.legend = FALSE  )+
  #scale_x_discrete(limits = Conditions) +  facet_wrap( ~Prefix) +
  ylab("dSPM") + 
  xlab("Conditions") +
  scale_fill_manual(values = c("blue" , "green" ,"red"))+
   geom_errorbar(aes(ymin=dSPM-SE,
                    ymax=dSPM+SE,
                 width=0.3))+ 
  theme_minimal() +
  theme(axis.text.x = element_text(size = 15, angle = 0, hjust = .5, vjust = .5, face = "plain"),
        axis.text.y = element_text(size = 15, angle = 0, hjust = 1, vjust = 0, face = "plain"),  
        axis.title.x = element_text(size = 18, angle = 0, hjust = .5, vjust = 0, face = "plain"),
        axis.title.y = element_text(size = 18, angle = 90, hjust = .5, vjust = .5, face = "plain"))

LH_OF_early_plot

Code 
knitr::kable(LH_OF_early)
Condition SE dSPM
CatViol 0.0411778 -0.1254958
Filler 0.0313287 -0.0434605
Gramm 0.0910601 0.1492997
SemViol 0.0756533 0.0737754